Title: README for: How do Biohackers Hack an Institution? Navigating Institutional Voids in the GCC Longevity Economy
Author: Mohammad Albous
Dataset DOI: 10.7910/DVN/6TPTHX

1. DATASET OVERVIEW
This dataset contains the supplementary methodological documentation, qualitative coding dictionaries, and anonymized participant data supporting the dual-case netnographic study on Shadow Market Shaping in the GCC.

2. NOTE ON "REPLICATION CODE"
Please note that this is primarily a qualitative, inductive netnographic study. Therefore, there are no execution scripts or software code files (e.g., R, Python, Stata) included. The "replication" logic for this study is fully contained within the qualitative codebook (Appendix D) and the mathematical parameters for the Institutional Void Premium (Appendix E).

3. FILE INVENTORY

Appendix A.docx: Details the full scope of the qualitative data corpus, temporal observation windows, and criteria for theoretical saturation.

Appendix B.docx: Contains the netnographic observation protocol and the researcher's positionality/reflexivity statement.

Appendix C.docx: The Data Translation Protocol and Semantic Equivalency Table, detailing how raw digital discourse was translated to protect informant anonymity while preserving cultural texture.

Appendix D.docx: The complete Qualitative Coding Dictionary, mapping raw data extracts to First-Order Concepts and Second-Order Themes.

Appendix E.docx: The Extended Institutional Void Premium (IVP) Dataset, containing the baseline prices, transaction costs, and economic friction calculations for restricted longevity interventions.

Anonymous_participants_data.xlsx: A tabular dataset mapping the network roles, primary platforms, and observed institutional evasion behaviors across the ~350 unique participant handles.

4. METHODOLOGICAL NOTES
To comply with the Association of Internet Researchers (AoIR) ethical guidelines for protecting informant identities in legally ambiguous digital spaces, all participant data in the Excel file has been strictly anonymized, and qualitative quotes in the appendices have been translated to remove digital fingerprints.